Prepare release of v0.9 (#937)

* prepare release

* hotfix manual

Author: mfeurer

autosklearn/__version__.py

@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "0.8.0"
+__version__ = "0.9.0"

doc/releases.rst

@@ -11,6 +11,37 @@
 Releases
 ========
 
+Version 0.9.0
+=============
+
+* ADD #157,#889: Improve handling of pandas dataframes, including the possibility to use pandas'
+  categorical column type.
+* ADD #375: New `SelectRates` feature preprocessing component for regression.
+* ADD #891: Improve the robustness of Auto-sklearn by using the single best model if no ensemble
+  is found.
+* ADD #902: Track performance of the ensemble over time.
+* ADD #914: Add an example on using pandas dataframes as input to Auto-sklearn.
+* ADD #919: Add an example for multilabel classification.
+* MAINT #909: Fix broken links in the documentation.
+* MAINT #907,#911: Add initial support for mypy.
+* MAINT #881,#927: Automatically build docker images on pushes to the master and development
+  branch and also push them to dockerhub and the github docker registry.
+* MAINT #918: Remove old dependencies from requirements.txt.
+* MAINT #931: Add information about the host system and installed packages to the log file.
+* MAINT #933: Reduce the number of warnings raised when building the documentation by sphinx.
+* MAINT #936: Completely restructure the examples section.
+* FIX #558: Provide better error message when the ensemble process fails due to a memory issue.
+* FIX #901: Allow custom resampling strategies again (was broken due to an upgrade of SMAC).
+* FIX #916: Fixes a bug where the data preprocessing configurations were ignored.
+* FIX #925: make internal data preprocessing objects clonable.
+
+Contributors v0.9.0
+*******************
+
+* Francisco Rivera
+* Matthias Feurer
+* felixleungsc
+
 Version 0.8
 ===========
 

examples/20_basic/example_classification.py

@@ -30,7 +30,6 @@
     per_run_time_limit=30,
     tmp_folder='/tmp/autosklearn_classification_example_tmp',
     output_folder='/tmp/autosklearn_classification_example_out',
-    ml_memory_limit=60,
 )
 automl.fit(X_train, y_train, dataset_name='breast_cancer')
 

examples/40_advanced/example_feature_types.py

@@ -10,7 +10,7 @@
 
 Additionally, you can provide a properly formatted pandas DataFrame, and the feature
 types will be automatically inferred, as demonstrated in
-`Pandas Train and Test inputs <examples/example_pandas_train_test.html>`_.
+`Pandas Train and Test inputs <example_pandas_train_test.html>`_.
 """
 import numpy as np
 

examples/40_advanced/example_get_pipeline_components.py

@@ -118,7 +118,7 @@
 ############################################################################
 # The only other important entry is the budget in case you are using
 # auto-sklearn with
-# `successive halving <examples/60_search/example_successive_halving.py>`_.
+# `successive halving <../60_search/example_successive_halving.py>`_.
 # The remaining parts of the key can be ignored for auto-sklearn and are
 # only there because the underlying optimizer, SMAC, can handle more general
 # problems, too.

examples/40_advanced/example_pandas_train_test.py

@@ -20,7 +20,7 @@
 There is also support to manually indicate the feature types (whether a column is categorical
 or numerical) via the argument feat_types from fit(). This is important when working with
 list or numpy arrays as there is no per-column dtype (further details in the example
-`Continuous and categorical data <examples/example_feature_types.html>`_).
+`Continuous and categorical data <example_feature_types.html>`_).
 """
 import time
 

examples/40_advanced/example_resampling.py

@@ -1,7 +1,7 @@
 """
-=======
-Holdout
-=======
+=====================
+Resampling Strategies
+=====================
 
 In *auto-sklearn* it is possible to use different resampling strategies
 by specifying the arguments ``resampling_strategy`` and
@@ -88,8 +88,8 @@
 # scikit-learn splitter objects
 # =============================
 # It is also possible to use
-# `<https://scikit-learn.org/stable/modules/classes.html#splitter-classes> scikit-learn's
-# splitter classes`_ to further customize the outputs.
+# `scikit-learn's splitter classes <https://scikit-learn.org/stable/modules/classes.html#splitter
+# -classes>`_ to further customize the outputs.
 
 
 resampling_strategy = sklearn.model_selection.RepeatedStratifiedKFold

examples/README.txt

@@ -1,5 +1,3 @@
-:orphan:
-
 .. _examples:
 
 ========

requirements.txt

@@ -13,4 +13,4 @@ pandas<1.0
 ConfigSpace>=0.4.0,<0.5
 pynisher>=0.4.2
 pyrfr>=0.7,<0.9
-smac>=0.12
+smac>=0.12.3